Open AccessAutomated retinal layer segmentation in OCT images of age‐related macular degeneration
Author(s) -
Chen Zailiang,
Li Dabao,
Shen Hailan,
Mo Yufang,
Wei Hao,
Ouyang Pingbo
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5304
Subject(s) - drusen , macular degeneration , artificial intelligence , segmentation , computer science , retinal pigment epithelium , optical coherence tomography , computer vision , retinal , deep learning , pattern recognition (psychology) , ophthalmology , medicine
Age‐related macular degeneration (AMD) is a common eye disease that causes progressive degeneration of the central vision. The presence of abundant drusen is a common early feature of AMD. Optical coherence tomography (OCT) can provide detailed structure information on drusen. The physiological structure of the retinal epithelium and drusen complex (RPEDC) and the Bruch's membrane (BM) layer boundaries will be influenced by the presence of drusen with AMD. Therefore, drusen quantification is important to diagnose and cure AMD. The authors proposed an automatic method to segment the inner limiting membrane, the retinal pigment epithelium and drusen complex (RPEDC) and BM layer boundaries from OCT images with AMD (termed as deep forest for layer segmentation (DF‐LS)). In their method, image patches are extracted and used to train a deep‐forest model to predict three boundary probability maps. In addition, they modify grapy theory and dynamic programming method to find the layer boundary. Finally, the layer boundary is smoothed by using a smoothing operation. The proposed DF‐LS method is evaluated on three publicly available datasets (one healthy dataset and two AMD dataset). The proposed DF‐LS method can yield superior mean unsigned error with an average error of 0.81 pixel on Tian et al .'s dataset, and 1.35, 1.23 pixel on Chiu et al .'s and Farisu et al .'s dataset, respectively.